Acquisition and Analysis of Aquatic Stroke Data From an Accelerometer Based System

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Abstract

The aim of this work was to develop devices for elite athletes to record performance related parameters during their training. A device was initially designed and built for rowing to record the motion of the boat. This was to gain understanding of motion signals in a one dimensional plane. The device uses a iPAQ handheld computer for recording and display of data to the user. Using the knowledge obtained from the accelerometer data of the rowing system an initial prototype device was designed and constructed for use in swimming. This device was required to be wearable whilst the swimmer was training, thus it had to record the data onboard. A second version of the swimming device was constructed to improve the usability of the device. The swimming device has fully sealed electronics, wireless charging and infrared communications. The device records three dimensional acceleration patterns at 150Hz, and can store over 6 hours of data using the internal memory. The device can operate for greater than 12 hours before needing to be recharged. The data collected from the swimming device was used to develop processing algorithms to extract when the swimmers push off from the wall, the type of stroke they are swimming, and for freestyle the stroke count. The results of the wall push off algorithm were compared against manual hand timing with 90% algorithm results being with ±1 second of the hand timing data. The stroke type identification algorithm determines which stroke is being swum and presently has an accuracy of 95%. The results of the freestyle stroke count algorithm were compared against manual stroke counts from raw accelerometers data and underwater video. Of the 164 data sets analysed over 90% of the algorithm results were within ±1 strokes of the manual recorded stroke counts.